Weather effects are often a contributing factor to the variability in utility usage forecasting. Weather effects can include past outdoor temperatures, past wind chill factors, past heat indices, and past precipitation measurement. Utilities can include natural gas providers, oil providers, electricity providers, and water supply providers. The modeling of weather effects, such as outdoor temperature, for a utility usage, such as electricity, allows for the electrical company to prepare for the commercial and residential demand in the short-term future. Utilizing previously recorded electrical usage for a given temperature allows for a compilation of data to be used to create a model representing a predicted electrical usage at a given temperature.
Modeling of weather effects involves specifying a mathematical formula for the relationship between usage and weather measurements. For example, the effect of the temperature on the utility usage is often described using a single index called “Degree Days” which can be further split into two categories, Heating Degree Days (HDD) and Cooling Degree Days (CDD). At a given instant in time, at most one of these indices will be relevant, and the other will have a zero value. For example, HDD is a measure of the severity of the cold weather as measured by the extent and duration of the temperature deviation below a baseline temperature (e.g., 15° C.) that is used to quantify the heating load. Similarly CDD is a measure of the severity of the hot weather as measured by the extent and duration of the temperature deviation above a baseline temperature (e.g., 20° C.) that is used to quantify the cooling load. Typically the utility usage is assumed to be correlated to the HDD or CDD. However, such a model for the relationship between the temperature and the utility usage is not ideal for calibrating accurate forecasting models, which are better modeled using nonlinear relationships.
In short-term forecasting, certain issues may arise with the ability to calibrate the forecasting models when information such as utility usage values is incomplete or missing for a range of weather effect values. For example, historical data for electrical usage may be unavailable for certain outlier values for the temperatures, such as for example, unseasonably hot or cold temperatures that may have never been encountered in the historical data. In another example, the amount of historical data for electrical usage might not include complete information if the historical data was taken from only a certain time period of the entire year. The appropriate electrical usage data might not exist for a temperature range that is typically not seen in the historical data during the certain season for which the calibrated model forecasts are required.